Person re-identification based on visual saliency

Person re-identification has long been a significant research direction in intelligent network surveillance. The challenging issues in person re-identification consist in pose, viewpoint and illumination changes and occlusions. In this paper, we propose a saliency based approach, which simulates the recognition process of the human brain, to tackle these issues. When people see a picture, they tend to focus on the salient areas and information in those areas is more determinant in the further matching and identification process. This so-called visual attention mechanism has long been studied and used in image segmentation, tracking, detection and recognition. To simulate this distinctive mechanism, we first calculate the saliency map which indicates the conspicuity of each pixel, and then we extract the saliency map weighted HSV histograms by giving each pixel a weight according to its saliency. We also design another feature, the salient colors, to address the occlusion problem. By opportunely combining these two features, our approach achieved state of the art performances.